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cs.LG2021
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability
Aviv Tamar, Daniel Soudry, Ev Zisselman
In the Bayesian reinforcement learning (RL) setting, a prior distribution over the unknown problem parameters -- the rewards and transitions -- is assumed, and a policy that optimi…
cs.LG2020
Deep Residual Flow for Out of Distribution Detection
Ev Zisselman, Aviv Tamar
The effective application of neural networks in the real-world relies on proficiently detecting out-of-distribution examples. Contemporary methods seek to model the distribution of…